Highly effective SPC programs combine technical competencies, such as using the right chart and sample size for the application, with good management principles such as ensuring operator involvement.

Driving Improvement in Process Improvement

Progressive manufacturers utilize Statistical Process Control to “listen” to their processes so that potentially harmful changes will be quickly detected and rectified. However, not all SPC programs deliver to their highest capability as there are many elements to get right to achieve maximum utility.

Highly effective SPC programs combine technical competencies, such as using the right chart and sample size for the application, with good management principles such as ensuring operator involvement. This article identifies seven key improvements that most companies can make to their SPC program. Several of the items have been addressed in greater detail in previous Ask the Expert articles and references to those articles are included.

1. Focus on the Right Characteristics to Control.

Clearly, controlling “everything” is not feasible or a smart use of limited resources. We must focus our efforts on controlling those process characteristics whose variation will impair product quality and/or reliability.

Furthermore, process control is most beneficial when we move upstream in the process. When the significant process variables that affect a key process output are being controlled, then the process output is predictable, allowing costly (and imperfect) inspection processes to be eliminated.

Determining exactly which key process input variable, if controlled, will produce predictable and consistent process outputs can be challenging. However, efficient and effective techniques such as Design of Experiments may be utilized to model the effect of input variables and their interactions on key characteristics.

2. Ensure Adequate Measurement Systems are Used

Effective use of data to drive decision-making requires adequate measurement systems. For example, when implementing statistical process control charts, we assume that a signal represents a significant change in the process and we react as such. However, inadequate measurement systems may result in inappropriate signals or even worse, charts that fail to detect important process changes. Thus, it is incumbent upon us to ensure that measurement systems are adequate for their intended use via proper assessments prior to their use. Only capable measurement systems should be utilized in data based methods such as Statistical Process Control.

3. Select the Right Chart for the Application.

Choosing the wrong type of chart is likely to result in problems diagnosing when special causes of variation are present in the system. Many factors should be considered when choosing a control chart for a given application. These include:

– The type of data being charted (continuous or attribute)
– The required sensitivity (size of the change to be detected) of the chart
– Whether the chart includes data from multiple locations or not
– The ease and cost of sampling
– Production volumes

4. Employ Effective Sampling Strategies.

The manner in which data is collected has a big impact on the performance of control charts. The whole point is to be able to adequately distinguish signals (special cause sources of variation) from noise (common cause sources of variation). Control limits should reflect the common cause variation only, so that special causes are actually detectable. Sampling schemes should be designed to ensure that special cause sources of variation, if they exist, are not reflected in the control limits. Here, special causes may be process changes or systematic differences between locations which produce part measurements, such as mold cavities, filling stations, or spindles. Traditional control charting methods such as Xbar and R charts assume that the sampling process produces rational samples. That is, all measurements come from a single distribution. When samples are rational, we are able to utilize the within subgroup variation (i.e. the range) to determine the limits of expected variation (i.e. control limits) on both charts.

5. Select the Right Sample Size.

Sample size is the number of measurements in each sample average. Charts of averages have the ability to vary their sensitivity to detect process changes based upon the sample size. I often get blank stares when I ask how a sample size was selected for a given chart. In fact, the sample size has a big impact on whether signals are likely to be obtained for a given process change such as a mean shift. Before we design a chart, we should have some idea of what kinds of process changes we would want to detect if they actually occurred. Usually this depends on how much variation we are willing to tolerate and how capable the current process is. A highly capable process will likely not need as much sensitivity as a borderline capable process. Selecting an appropriate sample size allows us to observe signals when we want them and minimize the impact of insignificant changes.

6. Empower Operators to Seek Improvements.

In some shops, operators are conditioned to view SPC as a stick to make sure they are doing their jobs. If a process goes out of control, the operator is blamed for “not controlling the process”. As a result, operators may frequently tweak (i.e., over-control) the process in an attempt to keep it on target. Instead, operators should receive adequate training in what SPC is all about and how it can help them manage the process effectively. They should be empowered to stop the process when appropriate and be rewarded for taking actions to identify and remove special cause sources of variation.

7. Manage the Charting Process and Drive Improvements.

Effective process control requires discipline in managing the charting process so that reaction plans are followed, systemic issues are identified, and improvements are realized over time. Managing the process involves reacting to chart signals, identifying root causes of process changes and systematically working to eliminate them. It also means knowing when to recalculate control limits and possibly modify sample sizes. When common cause variation is excessive, resulting in inadequate process capability, variation reduction efforts should be undertaken. Practitioners should not be shy about using additional tools, such as Design of Experiments, to identifying process settings that will minimize variability.

About DataNet Quality Systems

DataNet Quality Systems empowers manufacturers to improve products, processes, and profitability through real-time statistical software solutions. The company’s vision is to deliver trusted and capable technology solutions that allow manufacturers to create the highest quality product for the lowest possible cost. DataNet’s flagship product, WinSPC, provides statistical decision-making at the point of production and delivers real-time, actionable information to where it is needed most. With over 2500 customers worldwide and distributors across the globe, DataNet is dedicated to delivering a high level of customer service and support, shop-floor expertise, and training in the areas of Continuous Improvement, Six Sigma, and Lean Manufacturing services.